Advertisement

Autonomous Robots

, Volume 42, Issue 5, pp 1011–1021 | Cite as

Robot adaptation to human physical fatigue in human–robot co-manipulation

  • Luka PeternelEmail author
  • Nikos Tsagarakis
  • Darwin Caldwell
  • Arash Ajoudani
Article
Part of the following topical collections:
  1. Special Issue: Learning for Human-Robot Collaboration

Abstract

In this paper, we propose a novel method for human–robot collaboration, where the robot physical behaviour is adapted online to the human motor fatigue. The robot starts as a follower and imitates the human. As the collaborative task is performed under the human lead, the robot gradually learns the parameters and trajectories related to the task execution. In the meantime, the robot monitors the human fatigue during the task production. When a predefined level of fatigue is indicated, the robot uses the learnt skill to take over physically demanding aspects of the task and lets the human recover some of the strength. The human remains present to perform aspects of collaborative task that the robot cannot fully take over and maintains the overall supervision. The robot adaptation system is based on the Dynamical Movement Primitives, Locally Weighted Regression and Adaptive Frequency Oscillators. The estimation of the human motor fatigue is carried out using a proposed online model, which is based on the human muscle activity measured by the electromyography. We demonstrate the proposed approach with experiments on real-world co-manipulation tasks: material sawing and surface polishing.

Keywords

Physical human–robot collaboration Human fatigue Robot learning Human–robot interface 

Supplementary material

Supplementary material 1 (mp4 21007 KB)

References

  1. Agravante, D., Cherubini, A., Bussy, A., Gergondet, P., & Kheddar, A. (2014). Collaborative human-humanoid carrying using vision and haptic sensing. In Robotics and Automation (ICRA), 2014 IEEE International Conferene on (pp. 607–612).Google Scholar
  2. Ajoudani, A. (2016). Transferring human impedance regulation skills to robots. Berlin: Springer.CrossRefGoogle Scholar
  3. Ajoudani, A., Godfrey, S., Bianchi, M., Catalano, M., Grioli, G., Tsagarakis, N., et al. (2014). Exploring teleimpedance and tactile feedback for intuitive control of the pisa/iit softhand. IEEE Transactions on Haptics, 7(2), 203–215.CrossRefGoogle Scholar
  4. Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., & Hirzinger, G. (2007). The DLR lightweight robot: Design and control concepts for robots in human environments. Industrial Robot: An International Journal, 34(5), 376–385.CrossRefGoogle Scholar
  5. Albu-Schäffer, A., Ott, C., Frese, U., & Hirzinger, G. (2003). Cartesian impedance control of redundant robots: recent results with the DLR-light-weight-arms. In Robotics and Automation (ICRA), 2003 IEEE International Conference on (vol. 3, pp. 3704–3709).Google Scholar
  6. Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., & Peters, J. (2014). Interaction primitives for human–robot cooperation tasks. In Robotics and Automation (ICRA), 2014 IEEE International Conference on (pp. 2831–2837).Google Scholar
  7. Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001). The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature, 414(6862), 446–449.CrossRefGoogle Scholar
  8. De Luca, C. J. (1984). Myoelectrical manifestations of localized muscular fatigue in humans. Critical Reviews in Biomedical Engineering, 11(4), 251–279.Google Scholar
  9. Ding, J., Wexler, A. S., & Binder-Macleod, S. A. (2000). A predictive model of fatigue in human skeletal muscles. Journal of Applied Physiology, 89(4), 1322–1332.CrossRefGoogle Scholar
  10. Donner, P., & Buss, M. (2016). Cooperative swinging of complex pendulum-like objects: Experimental evaluation. IEEE Transactions on Robotics, 32(3), 744–753.CrossRefGoogle Scholar
  11. Dragan, A. D., & Srinivasa, S. S. (2013). A policy-blending formalism for shared control. The International Journal of Robotics Research, 32(7), 790–805.CrossRefGoogle Scholar
  12. Enoka, R. M., & Duchateau, J. (2008). Muscle fatigue: What, why and how it influences muscle function. The Journal of physiology, 586(1), 11–23.CrossRefGoogle Scholar
  13. Evrard, P., Gribovskaya, E., Calinon, S., Billard, A., & Kheddar, A. (2009). Teaching physical collaborative tasks: Object-lifting case study with a humanoid. In IEEE-RAS International Conference on Humanoid Robots (pp. 399–404).Google Scholar
  14. Fleischer, C., & Hommel, G. (2008). A human-exoskeleton interface utilizing electromyography. IEEE Transactions on Robotics, 24(4), 872–882.CrossRefGoogle Scholar
  15. Giat, Y., Mizrahi, J., & Levy, M. (1993). A musculotendon model of the fatigue profiles of paralyzed quadriceps muscle under fes. IEEE Transactions on Biomedical Engineering, 40(7), 664–674.CrossRefGoogle Scholar
  16. Gribovskaya, E., Kheddar, A., & Billard, A. (2011). Motion learning and adaptive impedance for robot control during physical interaction with humans. In Robotics and Automation (ICRA), 2011 IEEE International Conference on (pp. 4326–4332).Google Scholar
  17. Hogan, N. (1984). Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Transactions on Automatic Control, 29(8), 681–690.CrossRefzbMATHGoogle Scholar
  18. Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2003). Learning attractor landscapes for learning motor primitives. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems (pp. 1523–1530). Cambridge, MA: MIT Press.Google Scholar
  19. Ikemoto, S., Ben Amor, H., Minato, T., Jung, B., & Ishiguro, H. (2012). Physical human–robot interaction: Mutual learning and adaptation. IEEE Robotics Automation Magazine, 19(4), 24–35.CrossRefGoogle Scholar
  20. Ikeura, R., & Inooka, H. (1995). Variable impedance control of a robot for cooperation with a human. In Robotics and Automation (ICRA), 1995 IEEE International Conference on (vol. 3, pp. 3097–3102).Google Scholar
  21. Kaneko, K., Harada, K., Kanehiro, F., Miyamori, G., & Akachi, K. (2008). Humanoid robot HRP-3. In Intelligent Robots and Systems (IROS), 2008 IEEE/RSJ International Conference on (pp. 2471–2478).Google Scholar
  22. Kosuge, K. & Kazamura, N. (1997). Control of a robot handling an object in cooperation with a human. In Robot and Human Communication, 6th IEEE International Workshop on (pp. 142–147).Google Scholar
  23. Lee, D., & Ott, C. (2011). Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Autonomous Robots, 31(2), 115–131.CrossRefGoogle Scholar
  24. Liu, J. Z., Brown, R. W., & Yue, G. H. (2002). A dynamical model of muscle activation, fatigue, and recovery. Biophysical Journal, 82(5), 2344–2359.CrossRefGoogle Scholar
  25. Ma, L., Chablat, D., Bennis, F., & Zhang, W. (2009). A new simple dynamic muscle fatigue model and its validation. International Journal of Industrial Ergonomics, 39(1), 211–220.CrossRefGoogle Scholar
  26. Ma, L., Chablat, D., Bennis, F., Zhang, W., & Guillaume, F. (2010). A new muscle fatigue and recovery model and its ergonomics application in human simulation. Virtual and Physical Prototyping, 5(3), 123–137.CrossRefGoogle Scholar
  27. Maeda, G. J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., & Peters, J. (2017). Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks. Autonomous Robots, 41(3), 593–612.CrossRefGoogle Scholar
  28. Medina, J., Shelley, M., Lee, D., Takano, W., & Hirche, S. (2012). Towards interactive physical robotic assistance: Parameterizing motion primitives through natural language. In RO-MAN, 2012 IEEE (pp. 1097–1102).Google Scholar
  29. Nikolaidis, S., Kuznetsov, A., Hsu, D., & Srinivasa, S. (2016). Formalizing human–robot mutual adaptation: A bounded memory model. In 2016 11th ACM/IEEE International Conference on Human–Robot Interaction (HRI), (pp. 75–82).Google Scholar
  30. Peternel, L., & Babič, J. (2013). Learning of compliant human–robot interaction using full-body haptic interface. Advanced Robotics, 27(13), 1003–1012.CrossRefGoogle Scholar
  31. Peternel, L., Noda, T., Petrič, T., Ude, A., Morimoto, J., & Babič, J. (2016a). Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLoS ONE, 11(2), e0148942.CrossRefGoogle Scholar
  32. Peternel, L., Oztop, E., & Babič, J. (2016b). A shared control method for online human-in-the-loop robot learning based on locally weighted regression. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 3900–3906).Google Scholar
  33. Peternel, L., Petrič, T., Oztop, E., & Babič, J. (2014). Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach. Autonomous Robots, 36(1–2), 123–136.CrossRefGoogle Scholar
  34. Peternel, L., Tsagarakis, N., & Ajoudani, A. (2017). A human-robot co-manipulation approach based on human sensorimotor information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 811–822.CrossRefGoogle Scholar
  35. Peternel, L., Tsagarakis, N., Caldwell, D., & Ajoudani, A. (2016c). Adaptation of robot physical behaviour to human fatigue in human–robot co-manipulation. In IEEE-RAS International Conference on Humanoid Robots (pp. 489–494).Google Scholar
  36. Petrič, T., Gams, A., Ijspeert, A. J., & Žlajpah, L. (2011). On-line frequency adaptation and movement imitation for rhythmic robotic tasks. The International Journal of Robotics Research, 30(14), 1775–1788.CrossRefGoogle Scholar
  37. Rozo, L., Bruno, D., Calinon, S., Caldwell, D. G. (2015). Learning optimal controllers in human–robot cooperative transportation tasks with position and force constraints. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on.Google Scholar
  38. Sadrfaridpour, B., Saeidi, H., Burke, J., Madathil, K., & Wang, Y. (2016). Modeling and control of trust in human–robot collaborative manufacturing (pp. 115–141). Boston, MA.: Springer.Google Scholar
  39. Schaal, S., & Atkeson, C. G. (1998). Constructive incremental learning from only local information. Neural Computation, 10(8), 2047–2084.CrossRefGoogle Scholar
  40. Tsagarakis, N., Caldwell, D. G., Bicchi, A., Negrello, F., Garabini, M., Choi, W., et al. (2017). WALK-MAN: A high performance humanoid platform for realistic environments. Journal of Field Robotics, 34(7), 1225–1259.Google Scholar
  41. Tsumugiwa, T., Yokogawa, R., & Hara, K. (2002). Variable impedance control based on estimation of human arm stiffness for human–robot cooperative calligraphic task. In Robotics and Automation (ICRA), 2002 IEEE International Conference on (vol. 1, pp. 644–650).Google Scholar
  42. Turvey, M. (2007). Action and perception at the level of synergies. Human Movement Science, 26(4), 657–697.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.HRI² Lab and HHCM Lab, Department of Advanced RoboticsIstituto Italiano di TecnologiaGenoaItaly

Personalised recommendations